Establishing Trust in AI-Driven Data Observability and Quality Control: A Framework for Reliable and Scalable Standards

Abstract

The increasing reliance on Artificial Intelligence(AI) for data observability and quality control (QC) necessitates robust standards to ensure trustworthiness, reliability, and scalability. This paper introduces a detailed AI-driven data quality framework that integrates critical components such as data lineage tracking, interoperability standards, decentralized pipeline architecture, governance, and human-in-the-loop validation. Through this layered approach, the framework ensures scalability, traceability, and compliance, enhancing the trustworthiness of AI systems in production environments. We propose Data Trust Score (DTS) - a candidate IEEE-standard metric that quantifies trustworthiness through three pillars: Accuracy & Reliability, Explainability & Traceability, and Ethical & Governance Compliance. We showcase a comparative analysis with existing standards ISO/IEC 25012, NIST AI RMF, and IEEE P7003, illustrating the strengths of the proposed framework across scalability, real-time processing, explainability, and compliance readiness dimensions. The score supports progressive organizational adoption through integration with the Gartner AI Maturity Model. This work provides practical recommendations for evaluating AI-driven data observability systems across various industries.